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class ExampleDuplicateModel(nn.Module):
def __init__(self):
super().__init__()
self.param1 = nn.Parameter(torch.ones(1))
self.conv1 = nn.Sequential(nn.Conv2d(3, 4, kernel_size=1, bias=False))
self.conv2 = nn.Sequential(nn.Conv2d(4, 2, kernel_size=1))
self.bn = nn.BatchNorm... |
class PseudoDataParallel(nn.Module):
def __init__(self):
super().__init__()
self.module = ExampleModel()
def forward(self, x):
return x
|
def check_default_optimizer(optimizer, model, prefix=''):
assert isinstance(optimizer, torch.optim.SGD)
assert (optimizer.defaults['lr'] == base_lr)
assert (optimizer.defaults['momentum'] == momentum)
assert (optimizer.defaults['weight_decay'] == base_wd)
param_groups = optimizer.param_groups[0]
... |
def check_sgd_optimizer(optimizer, model, prefix='', bias_lr_mult=1, bias_decay_mult=1, norm_decay_mult=1, dwconv_decay_mult=1, dcn_offset_lr_mult=1, bypass_duplicate=False):
param_groups = optimizer.param_groups
assert isinstance(optimizer, torch.optim.SGD)
assert (optimizer.defaults['lr'] == base_lr)
... |
def test_default_optimizer_constructor():
model = ExampleModel()
with pytest.raises(TypeError):
optimizer_cfg = []
optim_constructor = DefaultOptimizerConstructor(optimizer_cfg)
optim_constructor(model)
with pytest.raises(TypeError):
optimizer_cfg = dict(lr=0.0001)
... |
def test_torch_optimizers():
torch_optimizers = ['ASGD', 'Adadelta', 'Adagrad', 'Adam', 'AdamW', 'Adamax', 'LBFGS', 'Optimizer', 'RMSprop', 'Rprop', 'SGD', 'SparseAdam']
assert set(torch_optimizers).issubset(set(TORCH_OPTIMIZERS))
|
def test_build_optimizer_constructor():
model = ExampleModel()
optimizer_cfg = dict(type='SGD', lr=base_lr, weight_decay=base_wd, momentum=momentum)
paramwise_cfg = dict(bias_lr_mult=2, bias_decay_mult=0.5, norm_decay_mult=0, dwconv_decay_mult=0.1, dcn_offset_lr_mult=0.1)
optim_constructor_cfg = dict(... |
def test_build_optimizer():
model = ExampleModel()
optimizer_cfg = dict(type='SGD', lr=base_lr, weight_decay=base_wd, momentum=momentum)
optimizer = build_optimizer(model, optimizer_cfg)
check_default_optimizer(optimizer, model)
model = ExampleModel()
optimizer_cfg = dict(type='SGD', lr=base_l... |
class OldStyleModel(nn.Module):
def __init__(self):
super().__init__()
self.conv = nn.Conv2d(3, 3, 1)
|
class Model(OldStyleModel):
def train_step(self):
pass
def val_step(self):
pass
|
def test_build_runner():
temp_root = tempfile.gettempdir()
dir_name = ''.join([random.choice(string.ascii_letters) for _ in range(10)])
default_args = dict(model=Model(), work_dir=osp.join(temp_root, dir_name), logger=logging.getLogger())
cfg = dict(type='EpochBasedRunner', max_epochs=1)
runner = ... |
@pytest.mark.parametrize('runner_class', RUNNERS.module_dict.values())
def test_epoch_based_runner(runner_class):
with pytest.warns(DeprecationWarning):
model = OldStyleModel()
def batch_processor():
pass
_ = runner_class(model, batch_processor, logger=logging.getLogger())
... |
@pytest.mark.parametrize('runner_class', RUNNERS.module_dict.values())
def test_runner_with_parallel(runner_class):
def batch_processor():
pass
model = MMDataParallel(OldStyleModel())
_ = runner_class(model, batch_processor, logger=logging.getLogger())
model = MMDataParallel(Model())
_ = ... |
@pytest.mark.parametrize('runner_class', RUNNERS.module_dict.values())
def test_save_checkpoint(runner_class):
model = Model()
runner = runner_class(model=model, logger=logging.getLogger())
with pytest.raises(TypeError):
runner.save_checkpoint('.', meta=list())
with tempfile.TemporaryDirectory... |
@pytest.mark.parametrize('runner_class', RUNNERS.module_dict.values())
def test_build_lr_momentum_hook(runner_class):
model = Model()
runner = runner_class(model=model, logger=logging.getLogger())
lr_config = dict(policy='CosineAnnealing', by_epoch=False, min_lr_ratio=0, warmup_iters=2, warmup_ratio=0.9)
... |
@pytest.mark.parametrize('runner_class', RUNNERS.module_dict.values())
def test_register_timer_hook(runner_class):
model = Model()
runner = runner_class(model=model, logger=logging.getLogger())
timer_config = None
runner.register_timer_hook(timer_config)
assert (len(runner.hooks) == 0)
timer_c... |
def test_set_random_seed():
set_random_seed(0)
a_random = random.randint(0, 10)
a_np_random = np.random.rand(2, 2)
a_torch_random = torch.rand(2, 2)
assert (torch.backends.cudnn.deterministic is False)
assert (torch.backends.cudnn.benchmark is False)
assert (os.environ['PYTHONHASHSEED'] ==... |
def test_construct():
cfg = Config()
assert (cfg.filename is None)
assert (cfg.text == '')
assert (len(cfg) == 0)
assert (cfg._cfg_dict == {})
with pytest.raises(TypeError):
Config([0, 1])
cfg_dict = dict(item1=[1, 2], item2=dict(a=0), item3=True, item4='test')
cfg_file = osp.j... |
def test_fromfile():
for filename in ['a.py', 'a.b.py', 'b.json', 'c.yaml']:
cfg_file = osp.join(data_path, 'config', filename)
cfg = Config.fromfile(cfg_file)
assert isinstance(cfg, Config)
assert (cfg.filename == cfg_file)
assert (cfg.text == ((osp.abspath(osp.expanduser(... |
def test_fromstring():
for filename in ['a.py', 'a.b.py', 'b.json', 'c.yaml']:
cfg_file = osp.join(data_path, 'config', filename)
file_format = osp.splitext(filename)[(- 1)]
in_cfg = Config.fromfile(cfg_file)
out_cfg = Config.fromstring(in_cfg.pretty_text, '.py')
assert (in... |
def test_merge_from_base():
cfg_file = osp.join(data_path, 'config/d.py')
cfg = Config.fromfile(cfg_file)
assert isinstance(cfg, Config)
assert (cfg.filename == cfg_file)
base_cfg_file = osp.join(data_path, 'config/base.py')
merge_text = ((osp.abspath(osp.expanduser(base_cfg_file)) + '\n') + o... |
def test_merge_from_multiple_bases():
cfg_file = osp.join(data_path, 'config/l.py')
cfg = Config.fromfile(cfg_file)
assert isinstance(cfg, Config)
assert (cfg.filename == cfg_file)
assert (cfg.item1 == [1, 2])
assert (cfg.item2.a == 0)
assert (cfg.item3 is False)
assert (cfg.item4 == '... |
def test_base_variables():
for file in ['t.py', 't.json', 't.yaml']:
cfg_file = osp.join(data_path, f'config/{file}')
cfg = Config.fromfile(cfg_file)
assert isinstance(cfg, Config)
assert (cfg.filename == cfg_file)
assert (cfg.item1 == [1, 2])
assert (cfg.item2.a ==... |
def test_merge_recursive_bases():
cfg_file = osp.join(data_path, 'config/f.py')
cfg = Config.fromfile(cfg_file)
assert isinstance(cfg, Config)
assert (cfg.filename == cfg_file)
assert (cfg.item1 == [2, 3])
assert (cfg.item2.a == 1)
assert (cfg.item3 is False)
assert (cfg.item4 == 'test... |
def test_merge_from_dict():
cfg_file = osp.join(data_path, 'config/a.py')
cfg = Config.fromfile(cfg_file)
input_options = {'item2.a': 1, 'item2.b': 0.1, 'item3': False}
cfg.merge_from_dict(input_options)
assert (cfg.item2 == dict(a=1, b=0.1))
assert (cfg.item3 is False)
cfg_file = osp.join... |
def test_merge_delete():
cfg_file = osp.join(data_path, 'config/delete.py')
cfg = Config.fromfile(cfg_file)
assert (cfg.item1 == dict(a=0))
assert (cfg.item2 == dict(a=0, b=0))
assert (cfg.item3 is True)
assert (cfg.item4 == 'test')
assert ('_delete_' not in cfg.item2)
assert (type(cfg... |
def test_merge_intermediate_variable():
cfg_file = osp.join(data_path, 'config/i_child.py')
cfg = Config.fromfile(cfg_file)
assert (cfg.item1 == [1, 2])
assert (cfg.item2 == dict(a=0))
assert (cfg.item3 is True)
assert (cfg.item4 == 'test')
assert (cfg.item_cfg == dict(b=2))
assert (cf... |
def test_fromfile_in_config():
cfg_file = osp.join(data_path, 'config/code.py')
cfg = Config.fromfile(cfg_file)
assert (cfg.cfg.item1 == [1, 2])
assert (cfg.cfg.item2 == dict(a=0))
assert (cfg.cfg.item3 is True)
assert (cfg.cfg.item4 == 'test')
assert (cfg.item5 == 1)
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def test_dict():
cfg_dict = dict(item1=[1, 2], item2=dict(a=0), item3=True, item4='test')
for filename in ['a.py', 'b.json', 'c.yaml']:
cfg_file = osp.join(data_path, 'config', filename)
cfg = Config.fromfile(cfg_file)
assert (len(cfg) == 4)
assert (set(cfg.keys()) == set(cfg_d... |
def test_setattr():
cfg = Config()
cfg.item1 = [1, 2]
cfg.item2 = {'a': 0}
cfg['item5'] = {'a': {'b': None}}
assert (cfg._cfg_dict['item1'] == [1, 2])
assert (cfg.item1 == [1, 2])
assert (cfg._cfg_dict['item2'] == {'a': 0})
assert (cfg.item2.a == 0)
assert (cfg._cfg_dict['item5'] =... |
def test_pretty_text():
cfg_file = osp.join(data_path, 'config/l.py')
cfg = Config.fromfile(cfg_file)
with tempfile.TemporaryDirectory() as temp_config_dir:
text_cfg_filename = osp.join(temp_config_dir, '_text_config.py')
with open(text_cfg_filename, 'w') as f:
f.write(cfg.pret... |
def test_dict_action():
parser = argparse.ArgumentParser(description='Train a detector')
parser.add_argument('--options', nargs='+', action=DictAction, help='custom options')
args = parser.parse_args(['--options', 'item2.a=a,b', 'item2.b=[(a,b), [1,2], false]'])
out_dict = {'item2.a': ['a', 'b'], 'ite... |
def test_dump_mapping():
cfg_file = osp.join(data_path, 'config/n.py')
cfg = Config.fromfile(cfg_file)
with tempfile.TemporaryDirectory() as temp_config_dir:
text_cfg_filename = osp.join(temp_config_dir, '_text_config.py')
cfg.dump(text_cfg_filename)
text_cfg = Config.fromfile(text... |
def test_reserved_key():
cfg_file = osp.join(data_path, 'config/g.py')
with pytest.raises(KeyError):
Config.fromfile(cfg_file)
|
def test_syntax_error():
temp_cfg_file = tempfile.NamedTemporaryFile(suffix='.py', delete=False)
temp_cfg_path = temp_cfg_file.name
with open(temp_cfg_path, 'w') as f:
f.write('a=0b=dict(c=1)')
with pytest.raises(SyntaxError, match='There are syntax errors in config file'):
Config.from... |
def test_pickle_support():
cfg_file = osp.join(data_path, 'config/n.py')
cfg = Config.fromfile(cfg_file)
with tempfile.TemporaryDirectory() as temp_config_dir:
pkl_cfg_filename = osp.join(temp_config_dir, '_pickle.pkl')
dump(cfg, pkl_cfg_filename)
pkl_cfg = load(pkl_cfg_filename)
... |
def test_deprecation():
deprecated_cfg_files = [osp.join(data_path, 'config/deprecated.py'), osp.join(data_path, 'config/deprecated_as_base.py')]
for cfg_file in deprecated_cfg_files:
with pytest.warns(DeprecationWarning):
cfg = Config.fromfile(cfg_file)
assert (cfg.item1 == 'expec... |
def test_deepcopy():
cfg_file = osp.join(data_path, 'config/n.py')
cfg = Config.fromfile(cfg_file)
new_cfg = copy.deepcopy(cfg)
assert isinstance(new_cfg, Config)
assert (new_cfg._cfg_dict == cfg._cfg_dict)
assert (new_cfg._cfg_dict is not cfg._cfg_dict)
assert (new_cfg._filename == cfg._f... |
def test_copy():
cfg_file = osp.join(data_path, 'config/n.py')
cfg = Config.fromfile(cfg_file)
new_cfg = copy.copy(cfg)
assert isinstance(new_cfg, Config)
assert (new_cfg is not cfg)
assert (new_cfg._cfg_dict is cfg._cfg_dict)
assert (new_cfg._filename == cfg._filename)
assert (new_cfg... |
def test_collect_env():
try:
import torch
except ModuleNotFoundError:
pytest.skip('skipping tests that require PyTorch')
from mmcv.utils import collect_env
env_info = collect_env()
expected_keys = ['sys.platform', 'Python', 'CUDA available', 'PyTorch', 'PyTorch compiling details', ... |
def test_load_url():
url1 = 'https://download.openmmlab.com/mmcv/test_data/saved_in_pt1.5.pth'
url2 = 'https://download.openmmlab.com/mmcv/test_data/saved_in_pt1.6.pth'
if (digit_version(TORCH_VERSION) < digit_version('1.7.0')):
model_zoo.load_url(url1)
with pytest.raises(RuntimeError):
... |
@patch('torch.distributed.get_rank', (lambda : 0))
@patch('torch.distributed.is_initialized', (lambda : True))
@patch('torch.distributed.is_available', (lambda : True))
def test_get_logger_rank0():
logger = get_logger('rank0.pkg1')
assert isinstance(logger, logging.Logger)
assert (len(logger.handlers) == ... |
@patch('torch.distributed.get_rank', (lambda : 1))
@patch('torch.distributed.is_initialized', (lambda : True))
@patch('torch.distributed.is_available', (lambda : True))
def test_get_logger_rank1():
logger = get_logger('rank1.pkg1')
assert isinstance(logger, logging.Logger)
assert (len(logger.handlers) == ... |
def test_print_log_print(capsys):
print_log('welcome', logger=None)
(out, _) = capsys.readouterr()
assert (out == 'welcome\n')
|
def test_print_log_silent(capsys, caplog):
print_log('welcome', logger='silent')
(out, _) = capsys.readouterr()
assert (out == '')
assert (len(caplog.records) == 0)
|
def test_print_log_logger(caplog):
print_log('welcome', logger='mmcv')
assert (caplog.record_tuples[(- 1)] == ('mmcv', logging.INFO, 'welcome'))
print_log('welcome', logger='mmcv', level=logging.ERROR)
assert (caplog.record_tuples[(- 1)] == ('mmcv', logging.ERROR, 'welcome'))
with tempfile.NamedTe... |
def test_print_log_exception():
with pytest.raises(TypeError):
print_log('welcome', logger=0)
|
def test_to_ntuple():
single_number = 2
assert (mmcv.utils.to_1tuple(single_number) == (single_number,))
assert (mmcv.utils.to_2tuple(single_number) == (single_number, single_number))
assert (mmcv.utils.to_3tuple(single_number) == (single_number, single_number, single_number))
assert (mmcv.utils.t... |
def test_iter_cast():
assert (mmcv.list_cast([1, 2, 3], int) == [1, 2, 3])
assert (mmcv.list_cast(['1.1', 2, '3'], float) == [1.1, 2.0, 3.0])
assert (mmcv.list_cast([1, 2, 3], str) == ['1', '2', '3'])
assert (mmcv.tuple_cast((1, 2, 3), str) == ('1', '2', '3'))
assert (next(mmcv.iter_cast([1, 2, 3]... |
def test_is_seq_of():
assert mmcv.is_seq_of([1.0, 2.0, 3.0], float)
assert mmcv.is_seq_of([(1,), (2,), (3,)], tuple)
assert mmcv.is_seq_of((1.0, 2.0, 3.0), float)
assert mmcv.is_list_of([1.0, 2.0, 3.0], float)
assert (not mmcv.is_seq_of((1.0, 2.0, 3.0), float, seq_type=list))
assert (not mmcv.... |
def test_slice_list():
in_list = [1, 2, 3, 4, 5, 6]
assert (mmcv.slice_list(in_list, [1, 2, 3]) == [[1], [2, 3], [4, 5, 6]])
assert (mmcv.slice_list(in_list, [len(in_list)]) == [in_list])
with pytest.raises(TypeError):
mmcv.slice_list(in_list, 2.0)
with pytest.raises(ValueError):
m... |
def test_concat_list():
assert (mmcv.concat_list([[1, 2]]) == [1, 2])
assert (mmcv.concat_list([[1, 2], [3, 4, 5], [6]]) == [1, 2, 3, 4, 5, 6])
|
def test_requires_package(capsys):
@mmcv.requires_package('nnn')
def func_a():
pass
@mmcv.requires_package(['numpy', 'n1', 'n2'])
def func_b():
pass
@mmcv.requires_package('numpy')
def func_c():
return 1
with pytest.raises(RuntimeError):
func_a()
(out... |
def test_requires_executable(capsys):
@mmcv.requires_executable('nnn')
def func_a():
pass
@mmcv.requires_executable(['ls', 'n1', 'n2'])
def func_b():
pass
@mmcv.requires_executable('mv')
def func_c():
return 1
with pytest.raises(RuntimeError):
func_a()
... |
def test_import_modules_from_strings():
import os.path as osp_
import sys as sys_
(osp, sys) = mmcv.import_modules_from_strings(['os.path', 'sys'])
assert (osp == osp_)
assert (sys == sys_)
osp = mmcv.import_modules_from_strings('os.path')
assert (osp == osp_)
assert (mmcv.import_modul... |
def test_is_method_overridden():
class Base():
def foo1():
pass
def foo2():
pass
class Sub(Base):
def foo1():
pass
assert mmcv.is_method_overridden('foo1', Base, Sub)
assert (not mmcv.is_method_overridden('foo2', Base, Sub))
sub_inst... |
def test_has_method():
class Foo():
def __init__(self, name):
self.name = name
def print_name(self):
print(self.name)
foo = Foo('foo')
assert (not has_method(foo, 'name'))
assert has_method(foo, 'print_name')
|
def test_deprecated_api_warning():
@deprecated_api_warning(name_dict=dict(old_key='new_key'))
def dummy_func(new_key=1):
return new_key
assert (dummy_func(old_key=2) == 2)
with pytest.raises(AssertionError):
dummy_func(old_key=1, new_key=2)
|
class TestJit(object):
def test_add_dict(self):
@mmcv.jit
def add_dict(oper):
rets = (oper['x'] + oper['y'])
return {'result': rets}
def add_dict_pyfunc(oper):
rets = (oper['x'] + oper['y'])
return {'result': rets}
a = torch.rand((... |
def test_is_filepath():
assert mmcv.is_filepath(__file__)
assert mmcv.is_filepath('abc')
assert mmcv.is_filepath(Path('/etc'))
assert (not mmcv.is_filepath(0))
|
def test_fopen():
assert hasattr(mmcv.fopen(__file__), 'read')
assert hasattr(mmcv.fopen(Path(__file__)), 'read')
|
def test_check_file_exist():
mmcv.check_file_exist(__file__)
with pytest.raises(FileNotFoundError):
mmcv.check_file_exist('no_such_file.txt')
|
def test_scandir():
folder = osp.join(osp.dirname(osp.dirname(__file__)), 'data/for_scan')
filenames = ['a.bin', '1.txt', '2.txt', '1.json', '2.json', '3.TXT']
assert (set(mmcv.scandir(folder)) == set(filenames))
assert (set(mmcv.scandir(Path(folder))) == set(filenames))
assert (set(mmcv.scandir(f... |
def reset_string_io(io):
io.truncate(0)
io.seek(0)
|
class TestProgressBar():
def test_start(self):
out = StringIO()
bar_width = 20
prog_bar = mmcv.ProgressBar(bar_width=bar_width, file=out)
assert (out.getvalue() == 'completed: 0, elapsed: 0s')
reset_string_io(out)
prog_bar = mmcv.ProgressBar(bar_width=bar_width, st... |
def sleep_1s(num):
time.sleep(1)
return num
|
def test_track_progress_list():
out = StringIO()
ret = mmcv.track_progress(sleep_1s, [1, 2, 3], bar_width=3, file=out)
assert (out.getvalue() == '[ ] 0/3, elapsed: 0s, ETA:\r[> ] 1/3, 1.0 task/s, elapsed: 1s, ETA: 2s\r[>> ] 2/3, 1.0 task/s, elapsed: 2s, ETA: 1s\r[>>>] 3/3, 1.0 task/s, elapsed: ... |
def test_track_progress_iterator():
out = StringIO()
ret = mmcv.track_progress(sleep_1s, ((i for i in [1, 2, 3]), 3), bar_width=3, file=out)
assert (out.getvalue() == '[ ] 0/3, elapsed: 0s, ETA:\r[> ] 1/3, 1.0 task/s, elapsed: 1s, ETA: 2s\r[>> ] 2/3, 1.0 task/s, elapsed: 2s, ETA: 1s\r[>>>] 3/3,... |
def test_track_iter_progress():
out = StringIO()
ret = []
for num in mmcv.track_iter_progress([1, 2, 3], bar_width=3, file=out):
ret.append(sleep_1s(num))
assert (out.getvalue() == '[ ] 0/3, elapsed: 0s, ETA:\r[> ] 1/3, 1.0 task/s, elapsed: 1s, ETA: 2s\r[>> ] 2/3, 1.0 task/s, elapsed: 2... |
def test_track_enum_progress():
out = StringIO()
ret = []
count = []
for (i, num) in enumerate(mmcv.track_iter_progress([1, 2, 3], bar_width=3, file=out)):
ret.append(sleep_1s(num))
count.append(i)
assert (out.getvalue() == '[ ] 0/3, elapsed: 0s, ETA:\r[> ] 1/3, 1.0 task/s, elap... |
def test_track_parallel_progress_list():
out = StringIO()
results = mmcv.track_parallel_progress(sleep_1s, [1, 2, 3, 4], 2, bar_width=4, file=out)
assert (results == [1, 2, 3, 4])
|
def test_track_parallel_progress_iterator():
out = StringIO()
results = mmcv.track_parallel_progress(sleep_1s, ((i for i in [1, 2, 3, 4]), 4), 2, bar_width=4, file=out)
assert (results == [1, 2, 3, 4])
|
def test_registry():
CATS = mmcv.Registry('cat')
assert (CATS.name == 'cat')
assert (CATS.module_dict == {})
assert (len(CATS) == 0)
@CATS.register_module()
class BritishShorthair():
pass
assert (len(CATS) == 1)
assert (CATS.get('BritishShorthair') is BritishShorthair)
cl... |
def test_multi_scope_registry():
DOGS = mmcv.Registry('dogs')
assert (DOGS.name == 'dogs')
assert (DOGS.scope == 'test_registry')
assert (DOGS.module_dict == {})
assert (len(DOGS) == 0)
@DOGS.register_module()
class GoldenRetriever():
pass
assert (len(DOGS) == 1)
assert (D... |
def test_build_from_cfg():
BACKBONES = mmcv.Registry('backbone')
@BACKBONES.register_module()
class ResNet():
def __init__(self, depth, stages=4):
self.depth = depth
self.stages = stages
@BACKBONES.register_module()
class ResNeXt():
def __init__(self, de... |
def test_assert_dict_contains_subset():
dict_obj = {'a': 'test1', 'b': 2, 'c': (4, 6)}
expected_subset = {'a': 'test1', 'b': 2, 'c': (4, 6)}
assert mmcv.assert_dict_contains_subset(dict_obj, expected_subset)
expected_subset = {'a': 'test1', 'b': 2, 'c': (6, 4)}
assert (not mmcv.assert_dict_contain... |
def test_assert_attrs_equal():
class TestExample(object):
(a, b, c) = (1, ('wvi', 3), [4.5, 3.14])
def test_func(self):
return self.b
assert mmcv.assert_attrs_equal(TestExample, {'a': 1, 'b': ('wvi', 3), 'c': [4.5, 3.14]})
assert (not mmcv.assert_attrs_equal(TestExample, {'a'... |
@pytest.mark.parametrize('obj', assert_dict_has_keys_data_1)
@pytest.mark.parametrize('expected_keys, ret_value', assert_dict_has_keys_data_2)
def test_assert_dict_has_keys(obj, expected_keys, ret_value):
assert (mmcv.assert_dict_has_keys(obj, expected_keys) == ret_value)
|
@pytest.mark.parametrize('result_keys', assert_keys_equal_data_1)
@pytest.mark.parametrize('target_keys, ret_value', assert_keys_equal_data_2)
def test_assert_keys_equal(result_keys, target_keys, ret_value):
assert (mmcv.assert_keys_equal(result_keys, target_keys) == ret_value)
|
@pytest.mark.skipif((torch is None), reason='requires torch library')
def test_assert_is_norm_layer():
assert (not mmcv.assert_is_norm_layer(nn.Conv3d(3, 64, 3)))
assert mmcv.assert_is_norm_layer(nn.BatchNorm3d(128))
assert mmcv.assert_is_norm_layer(nn.GroupNorm(8, 64))
assert (not mmcv.assert_is_norm... |
@pytest.mark.skipif((torch is None), reason='requires torch library')
def test_assert_params_all_zeros():
demo_module = nn.Conv2d(3, 64, 3)
nn.init.constant_(demo_module.weight, 0)
nn.init.constant_(demo_module.bias, 0)
assert mmcv.assert_params_all_zeros(demo_module)
nn.init.xavier_normal_(demo_m... |
def test_check_python_script(capsys):
mmcv.utils.check_python_script('./tests/data/scripts/hello.py zz')
captured = capsys.readouterr().out
assert (captured == 'hello zz!\n')
mmcv.utils.check_python_script('./tests/data/scripts/hello.py agent')
captured = capsys.readouterr().out
assert (captur... |
def test_timer_init():
timer = mmcv.Timer(start=False)
assert (not timer.is_running)
timer.start()
assert timer.is_running
timer = mmcv.Timer()
assert timer.is_running
|
def test_timer_run():
timer = mmcv.Timer()
time.sleep(1)
assert (abs((timer.since_start() - 1)) < 0.01)
time.sleep(1)
assert (abs((timer.since_last_check() - 1)) < 0.01)
assert (abs((timer.since_start() - 2)) < 0.01)
timer = mmcv.Timer(False)
with pytest.raises(mmcv.TimerError):
... |
def test_timer_context(capsys):
with mmcv.Timer():
time.sleep(1)
(out, _) = capsys.readouterr()
assert (abs((float(out) - 1)) < 0.01)
with mmcv.Timer(print_tmpl='time: {:.1f}s'):
time.sleep(1)
(out, _) = capsys.readouterr()
assert (out == 'time: 1.0s\n')
|
@pytest.mark.skipif((digit_version(torch.__version__) < digit_version('1.6.0')), reason='torch.jit.is_tracing is not available before 1.6.0')
def test_is_jit_tracing():
def foo(x):
if is_jit_tracing():
return x
else:
return x.tolist()
x = torch.rand(3)
assert isins... |
def test_digit_version():
assert (digit_version('0.2.16') == (0, 2, 16, 0, 0, 0))
assert (digit_version('1.2.3') == (1, 2, 3, 0, 0, 0))
assert (digit_version('1.2.3rc0') == (1, 2, 3, 0, (- 1), 0))
assert (digit_version('1.2.3rc1') == (1, 2, 3, 0, (- 1), 1))
assert (digit_version('1.0rc0') == (1, 0... |
def test_parse_version_info():
assert (parse_version_info('0.2.16') == (0, 2, 16, 0, 0, 0))
assert (parse_version_info('1.2.3') == (1, 2, 3, 0, 0, 0))
assert (parse_version_info('1.2.3rc0') == (1, 2, 3, 0, 'rc', 0))
assert (parse_version_info('1.2.3rc1') == (1, 2, 3, 0, 'rc', 1))
assert (parse_ver... |
def _mock_cmd_success(cmd):
return '3b46d33e90c397869ad5103075838fdfc9812aa0'.encode('ascii')
|
def _mock_cmd_fail(cmd):
raise OSError
|
def test_get_git_hash():
with patch('mmcv.utils.version_utils._minimal_ext_cmd', _mock_cmd_success):
assert (get_git_hash() == '3b46d33e90c397869ad5103075838fdfc9812aa0')
assert (get_git_hash(digits=6) == '3b46d3')
assert (get_git_hash(digits=100) == get_git_hash())
with patch('mmcv.ut... |
class TestVideoEditor():
@classmethod
def setup_class(cls):
cls.video_path = osp.join(osp.dirname(__file__), '../data/test.mp4')
cls.num_frames = 168
@pytest.mark.skipif((platform.system() == 'Windows'), reason='skip windows')
def test_cut_concat_video(self):
part1_file = osp... |
class TestCache():
def test_init(self):
with pytest.raises(ValueError):
mmcv.Cache(0)
cache = mmcv.Cache(100)
assert (cache.capacity == 100)
assert (cache.size == 0)
def test_put(self):
cache = mmcv.Cache(3)
for i in range(1, 4):
cache.... |
class TestVideoReader():
@classmethod
def setup_class(cls):
cls.video_path = osp.join(osp.dirname(__file__), '../data/test.mp4')
cls.num_frames = 168
cls.video_url = 'https://www.learningcontainer.com/wp-content/uploads/2020/05/sample-mp4-file.mp4'
def test_load(self):
v ... |
def test_color():
assert (mmcv.color_val(mmcv.Color.blue) == (255, 0, 0))
assert (mmcv.color_val('green') == (0, 255, 0))
assert (mmcv.color_val((1, 2, 3)) == (1, 2, 3))
assert (mmcv.color_val(100) == (100, 100, 100))
assert (mmcv.color_val(np.zeros(3, dtype=int)) == (0, 0, 0))
with pytest.rai... |
def digit_version(version_str):
digit_version = []
for x in version_str.split('.'):
if x.isdigit():
digit_version.append(int(x))
elif (x.find('rc') != (- 1)):
patch_version = x.split('rc')
digit_version.append((int(patch_version[0]) - 1))
digit_v... |
def init_detector(config, checkpoint=None, device='cuda:0', cfg_options=None):
'Initialize a detector from config file.\n\n Args:\n config (str or :obj:`mmcv.Config`): Config file path or the config\n object.\n checkpoint (str, optional): Checkpoint path. If left as None, the model\n ... |
class LoadImage():
'Deprecated.\n\n A simple pipeline to load image.\n '
def __call__(self, results):
'Call function to load images into results.\n\n Args:\n results (dict): A result dict contains the file name\n of the image to be read.\n Returns:\n ... |
def inference_detector(model, imgs):
'Inference image(s) with the detector.\n\n Args:\n model (nn.Module): The loaded detector.\n imgs (str/ndarray or list[str/ndarray] or tuple[str/ndarray]):\n Either image files or loaded images.\n\n Returns:\n If imgs is a list or tuple, th... |
def show_result_pyplot(model, img, result, score_thr=0.3, title='result', wait_time=0, palette=None):
'Visualize the detection results on the image.\n\n Args:\n model (nn.Module): The loaded detector.\n img (str or np.ndarray): Image filename or loaded image.\n result (tuple[list] or list)... |
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